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An increasing number of location-based service providers are taking the advantage of cloud computing by outsourcing their Point of Interest (POI) datasets and query services to third-party cloud service providers (CSPs), which answer various location-based queries from users on their behalf. A critical security challenge is to ensure the integrity and completeness of any query result returned by CSPs. As an important type of queries, a location-based skyline query (LBSQ) asks for the POIs not dominated by any other POI with respect to a given query position, i.e., no POI is both closer to the query position and more preferable with respect to a given numeric attribute. While there have been several recent attempts on authenticating outsourced LBSQ, none of them support the shortest path distance that is preferable to the Euclidian distance in metropolitan areas. In this paper, we tackle this open challenge by introducing AuthSkySP, a novel scheme for authenticating outsourced LBSQ under the shortest path distance, which allows the user to verify the integrity and completeness of any LBSQ result returned by an untrusted CSP. We confirm the effectiveness and efficiency of our proposed solution via detailed experimental studies using both real and synthetic datasets. 

IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average. 

IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average. 

Abstract We present a JWST MIRI medium-resolution spectrometer spectrum (5–27μm) of the Type Ia supernova (SN Ia) SN 2021aefx at +415 days pastB-band maximum. The spectrum, which was obtained during the iron-dominated nebular phase, has been analyzed in combination with previous JWST observations of SN 2021aefx to provide the first JWST time series analysis of an SN Ia. We find that the temporal evolution of the [Coiii] 11.888μm feature directly traces the decay of⁵⁶Co. The spectra, line profiles, and their evolution are analyzed with off-center delayed-detonation models. Best fits were obtained with white dwarf (WD) central densities ofρ_c= 0.9−1.1 × 10⁹g cm⁻³, a WD mass ofM_WD= 1.33–1.35M_⊙, a WD magnetic field of ≈10⁶G, and an off-center deflagration-to-detonation transition at ≈0.5M_⊙seen opposite to the line of sight of the observer (−30°). The inner electron capture core is dominated by energy deposition fromγ-rays, whereas a broader region is dominated by positron deposition, placing SN 2021aefx at +415 days in the transitional phase of the evolution to the positron-dominated regime. The formerly “flat-tilted” profile at 9μm now has a significant contribution from [Niiv], [Feii], and [Feiii] and less from [Ariii], which alters the shape of the feature as positrons mostly excite the low-velocity Ar. Overall, the strength of the stable Ni features in the spectrum is dominated by positron transport rather than the Ni mass. Based on multidimensional models, our analysis is consistent with a single-spot, close-to-central ignition with an indication of a preexisting turbulent velocity field and excludes a multiple-spot, off-center ignition.